Lead Consultant - MLOps Engineer

In this role, you will define, implement and oversee the MLOps strategy for scalable, compliant, and cost-efficient deployment of AI/GenAI models across the enterprise. This role combines deep DevOps knowledge, infrastructure architecture, and AI platform design to guide how teams build and ship ML models securely and reliably.

You will establish governance, reuse, and automation frameworks for AI infrastructure, including Terraform-first cloud automation, multi-environment CI/CD, and observability pipelines.

Responsibilities

1. Architect secure, reusable, modular IaC frameworks across cloud and regions for MLOps
2. Lead the development of CI/CD pipelines and standardize deployment frameworks.
3. Design observability and monitoring systems for ML/GenAI workloads.
4. Collaborate with platform, data science, compliance and Enterprise Architecture teams to ensure scalable ML operations.
5. Define enterprise-wide MLOps architecture and standards (build → deploy → monitor)
6. Lead design of GenAI/LLMOps platform (Bedrock/OpenAI/Hugging Face + RAG stack)
7. Integrate governance controls (approvals, drift detection, rollback strategies)
8. Define model metadata standards, monitoring SLAs, and re-training workflows
9. Influence tooling, hiring, and roadmap decisions for AI/ML delivery
10. Be engaging in the design, development and maintenance of data pipelines for various AI use cases
11. Required to actively contribution to key deliverables as part of an agile development team

Qualifications we seek in you!

Minimum Qualifications

1. Good years of experience in DevOps or MLOps roles.
2. Degree/qualification in Computer Science or a related field, or equivalent work experience
3. Strong Python programming skills.
4. Hands on experience in containerised deployment.
5. Proficient with AWS (SageMaker, Lambda, ECR), Terraform, and Python.
6. Demonstrated experience deploying multiple GenAI systems into production.
7. Hands-on experience deploying 3–4 ML/GenAI models in AWS.
8. Deep understanding of ML model lifecycle: train → test → deploy → monitor → retrain.
9. Experience in developing, testing, and deploying data pipelines using public cloud.
10. Clear and effective communication skills to interact with team members, stakeholders and end users
11. Knowledge of governance and compliance policies, standards, and procedures
12. Exposure to RAG/LLM workloads and model deployment infrastructure.
13. Experience in developing, testing, and deploying data pipelines

Preferred Qualifications/ Skills

1. Experience designing model governance frameworks and CI/CD pipelines.
2. Knowledge of governance and compliance policies, standards, and procedures
3. Advanced understanding of platform security, cost optimization, and ML observability.